A system approach for closed-loop assessment of neuro-visual function based on convolutional neural network analysis of EEG signals
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1136008.pdf
Description
Published version
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4.44 MB
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Checksum (MD5)
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Author(s)
Stock, Simon C.
Armengol-Urpi, Alexandre
Kovacs, Balint
Maier, Heiko
Gerdes, Marius
Stork, Wilhelm
Sarma, Sanjay E.
Date Issued
April 1, 2020
Journal
Proceedings of SPIE - The International Society for Optical Engineering
Publisher
SPIE
Citation
Stock, Simon C., Armengol-Urpi, Alexandre, Kovacs, Balint, Maier, Heiko, Gerdes, Marius et al. 2020. "A system approach for closed-loop assessment of neuro-visual function based on convolutional neural network analysis of EEG signals." Proceedings of SPIE - The International Society for Optical Engineering, 11360.
Version
Final published version
Abstract
© 2020 SPIE. We propose a generalized, modular, closed-loop system for objective assessment of human visual parameters. Our system presents periodical visual stimuli to the patient's field of view and analyses the consequent evoked brain potentials elicited in the occipital lobe and recorded through EEG. The analysis of the monitored EEG data is performed in an end-to-end fashion by a convolutional neural network (CNN). We propose a novel CNN architecture for EEG signal analysis that can be trained utilizing the benefits of multi-task learning. The closedloop attribute of our system allows for a real-time adaptation of the subsequent stimuli to further examine a potentially damaged area or increase the granularity of the exploration. Interchangeability is provided in terms of software modules, stimulus type, visual hardware, EEG acquisition device and EEG electrodes. Initially, the system is designed to monitor visual field loss originating from glaucoma or damage to the optic nerve using a virtual reality (VR) headset for the stimuli presentation. The modular architecture of our system paves the way for the assessment and monitoring of other neuro-visual functions.
MIT Department
Massachusetts Institute of Technology. Department of Mechanical Engineering
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DOI of Published Version
https://doi.org/10.1117/12.2554417
